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Abstract

Text mining, a subfield of natural language processing (NLP), has received considerable attention in recent years due to its ability to extract valuable insights from large volumes of unstructured textual data. This review aims to provide a comprehensive evaluation of the applicability of text mining techniques across various domains and industries. The reviewstarts off with a dialogue of the basic ideas and methodologies that are concerned with textual content mining together with preprocessing, feature extraction, and machine learning algorithms. Furthermore, this survey highlights the challenges faced at some stage in implementing textual content mining strategies. Additionally, the review explores emerging tendencies and possibilities in text-mining research. It discusses advancements in deep learning models for text evaluation, integration with different AI technologies like image or speech recognition for multimodal analysis, utilization of domain-unique ontologies or information graphs for more desirable information of textual facts, and incorporation of explainable AI strategies to improve interpretability. The findings from this overview are analyzed to identify common developments and patterns in text mining packages across extraordinary domain names.The consequences of this paper will advantage researchers by means of imparting updated expertise of modern practices in textual content mining. Additionally, it will manual practitioners in selecting suitable strategies for their unique application domain names while addressing capacity-demanding situations.

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